Quality Control
2018-06-18
# Last modified 2018-05-24
bcbioRNASeq::prepareRNASeqTemplate()
source("_setup.R")
# Directory paths ==============================================================
invisible(mapply(
FUN = dir.create,
path = c(params$data_dir, params$results_dir),
MoreArgs = list(showWarnings = FALSE, recursive = TRUE)
))
# Load object ==================================================================
bcb_name <- load(params$bcb_file)
bcb <- get(bcb_name, inherits = FALSE)
stopifnot(is(bcb, "bcbioRNASeq"))
invisible(validObject(bcb))Overview
- Principal Investigator:
- Researcher:
- Experiment:
bcbio run data was imported from /Users/travis/build/hbc/bcbio_rnaseq_output_example.
Count matrices
> raw_counts <- counts(bcb, normalized = FALSE)
> # DESeq2 normalized counts
> normalized_counts <- counts(bcb, normalized = TRUE)
> # Transcripts per million
> tpm <- counts(bcb, normalized = "tpm")
> saveData(raw_counts, normalized_counts, tpm, dir = params$data_dir)## Saving raw_counts.rda, normalized_counts.rda, tpm.rda to /Users/travis/build/hbc/bcbio_rnaseq_output_example/report/data## Writing raw_counts, normalized_counts, tpm to /Users/travis/build/hbc/bcbio_rnaseq_output_example/report/results/2018-06-18/countsThe results are saved as gzip-compressed comma separated values (CSV). Gzip compression is natively supported on macOS and Linux-based operating systems. If you’re running Windows, we recommend installing 7-Zip. CSV files can be opened in Excel or RStudio.
normalized_counts.csv.gz: Use to evaluate individual genes and/or generate plots. These counts are normalized for the variation in sequencing depth across samples.tpm.csv.gz: Transcripts per million, scaled by length and also suitable for plotting.raw_counts.csv.gz: Only use to perform a new differential expression analysis. These counts will vary across samples due to differences in sequencing depth, and have not been normalized. Do not use this file for plotting genes.
Read metrics
Mapped reads
The number of mapped reads should correspond to the number of total reads.
Mapping rate
The genomic mapping rate represents the percentage of reads mapping to the reference genome. Low mapping rates are indicative of sample contamination, poor sequencing quality or other artifacts.
Gene detection saturation
We should observe a linear trend in the number of genes detected with the number of mapped reads, which indicates that the sample input was not overloaded.
Exonic mapping rate
Ideally, at least 60% of total reads should map to exons.
Intronic mapping rate
The majority of reads should map to exons and not introns.
rRNA mapping rate
Samples should have a ribosomal RNA (rRNA) contamination rate below 10%.
Counts per gene
Generally, we expect similar count spreads for all genes between samples unless the library sizes or total RNA expression are different. The log10 TMM-normalized counts per gene normalization method (Robinson and Oshlack 2010) equates the overall expression levels of genes between samples under the assumption that the majority of them are not differentially expressed. Therefore, by normalizing for total RNA expression by sample, we expect the spread of the log10 TMM-normalized counts per gene to be similar for every sample.
## 29858 non-zero genes detected## Applying trimmed mean of M-values (TMM) normalization
TPM per biotype
Different RNA-seq processing methods can preferentially capture a subset of the RNA species from the total RNA. For example, polyA selection should select for mostly coding genes and skip a large percentage of non-polyA non-coding RNA. Here we make boxplots of the TPM for the top 12 biotypes with the most genes assigned to them for each sample.
TPM per broad biotype class
The Ensembl biotype clasifications are too specific to plot them all. Here we have grouped the biotypes into broad classes and plot boxplots of the TPM for each sample.
Count density
Generally, we expect similar count spreads for all genes between samples unless the total expressed RNA per sample is different.
## 29858 non-zero genes detected## Applying trimmed mean of M-values (TMM) normalization
Fit modeling
Several quality metrics are first assessed to explore the fit of the model, before differential expression analysis is performed.
Dispersion
The following plot shows the dispersion by mean of normalized counts. We expect the dispersion to decrease as the mean of normalized counts increases.
## converting counts to integer mode## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing
Variance stabilization
The plots below show the standard deviation of normalized counts (normalized_counts) using log2(), rlog(), and variance stabilizing (vst()) transformations by rank(mean). The transformations greatly reduce the standard deviation, with rlog() stabilizing the variance best across the mean.
Sample similarity analysis
Before performing similarity analysis, we transform counts to log2, which acts to minimize large differences in sequencing depth and helps normalize all samples to a similar dynamic range. For RNA-seq count data, variance increases with the mean. Logarithmic transformation of normalized count values with a small pseudocount will account for large variations seen between the highest expressing genes so that these genes won’t dominate the PCA plots. However, due to the strong noise among low count values due to Poisson, the general log2 transformation will amplify this noise, and instead, low count genes will now dominate the PCA plots. So instead, we use a regularized log (“rlog”; base 2) transformation that gives similar results for high counts as a log2 transformation but also shrinks the values of low counts towards the genes’ average across samples. We do this with the rlog() function in the DESeq2 package (Love, Huber, and Anders 2014), which we will later use for differential gene expression analysis.
Principal component analysis (PCA)
PCA (Jolliffe 2002) is a multivariate technique that allows us to summarize the systematic patterns of variations in the data. PCA takes the expression levels for genes and transforms it in principal component space, reducing each sample into one point. Thereby, we can separate samples by expression variation, and identify potential sample outliers. The PCA plot is a way to look at how samples are clustering.
unlabeled
## Using vst counts## Plotting PCA using 500 most variable genes
labeled
## Using vst counts
## Plotting PCA using 500 most variable genes
Covariates correlation with PCs
When multiple factors may influence the results of a given experiment, it is useful to assess which of them is responsible for the most variance as determined by PCA. We adapted the method described by Daily et al. where they integrated a method to correlate covariates with principal components values to determine the importance of each factor.
Here we are showing the correlational analysis of the rlog transformed count data’s principal components with the metadata covariates of interest. Significant correlations (FDR < 0.1) are shaded from blue (anti-correlated) to orange (correlated), with non-significant correlations shaded in gray.
##
## running pca and calculating correlations for:
## un-scaled data in pca;
## pve >= 5%;
## kendall cor
Hierarchical clustering
Inter-correlation analysis (ICA) is another way to look at how well samples cluster by plotting the correlation between the expression profiles of the samples.
## Using vst counts
Methods
RNA-seq counts were generated by bcbio and bcbioRNASeq using salmon (Patro et al. 2017). Counts were imported into R using tximport (Soneson, Love, and Robinson 2016) and DESeq2 (Love, Huber, and Anders 2014). Gene annotations were obtained from Ensembl. Plots were generated by ggplot2 (Wickham 2009). Heatmaps were generated by pheatmap (Kolde 2015).
R session information
YAML params
## $results_dir
## [1] "results/2018-06-18/counts"
##
## $bcb_file
## bcb
## "/Users/travis/build/hbc/bcbio_rnaseq_output_example/report/data/bcb.rda"
##
## $data_dir
## [1] "data"_setup.R
## library(bcbioRNASeq)
## library(DESeq2)
## library(DEGreport)
##
## library(knitr)
## library(tidyverse)
##
## # Set seed for reproducibility
## set.seed(1454944673L)
##
## opts_chunk[["set"]](
## autodep = TRUE,
## bootstrap.show.code = FALSE,
## cache = TRUE,
## cache.lazy = TRUE,
## dev = c("png", "pdf"),
## error = TRUE,
## fig.height = 10L,
## fig.retina = 2L,
## fig.width = 10L,
## highlight = TRUE,
## message = TRUE,
## prompt = TRUE,
## tidy = FALSE,
## warning = TRUE
## )
##
## theme_set(
## theme_paperwhite(
## base_size = 14L,
## legend_position = "bottom"
## )
## )sessioninfo::session_info()
## Warning: 'DESCRIPTION' file has an 'Encoding' field and re-encoding is not
## possible## ─ Session info ──────────────────────────────────────────────────────────
## setting value
## version R version 3.5.0 (2018-04-23)
## os macOS Sierra 10.12.6
## system x86_64, darwin15.6.0
## ui X11
## language (EN)
## collate en_US.UTF-8
## tz GMT
## date 2018-06-18
##
## ─ Packages ──────────────────────────────────────────────────────────────
## package * version date
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## R version 3.5.0 (2018-04-23)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Sierra 10.12.6
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] hexbin_1.27.2 forcats_0.3.0
## [3] stringr_1.3.1 dplyr_0.7.5
## [5] purrr_0.2.5 readr_1.1.1
## [7] tidyr_0.8.1 tibble_1.4.2
## [9] ggplot2_2.2.1 tidyverse_1.2.1
## [11] knitr_1.20 DEGreport_1.16.0
## [13] quantreg_5.36 SparseM_1.77
## [15] bindrcpp_0.2.2 bcbioRNASeq_0.2.4
## [17] DESeq2_1.20.0 bcbioBase_0.2.15
## [19] basejump_0.5.9 SummarizedExperiment_1.10.1
## [21] DelayedArray_0.6.1 BiocParallel_1.14.1
## [23] matrixStats_0.53.1 Biobase_2.40.0
## [25] GenomicRanges_1.32.3 GenomeInfoDb_1.16.0
## [27] IRanges_2.14.10 S4Vectors_0.18.3
## [29] BiocGenerics_0.26.0 rmarkdown_1.10
## [31] testthat_2.0.0
##
## loaded via a namespace (and not attached):
## [1] R.utils_2.6.0 tidyselect_0.2.4
## [3] RSQLite_2.1.1 AnnotationDbi_1.42.1
## [5] htmlwidgets_1.2 grid_3.5.0
## [7] munsell_0.4.3 preprocessCore_1.42.0
## [9] codetools_0.2-15 withr_2.1.2
## [11] colorspace_1.3-2 BiocInstaller_1.30.0
## [13] rstudioapi_0.7 assertive.base_0.0-7
## [15] rdrop2_0.8.1 labeling_0.3
## [17] tximport_1.8.0 GenomeInfoDbData_1.1.0
## [19] mnormt_1.5-5 bit64_0.9-7
## [21] pheatmap_1.0.10 rprojroot_1.3-2
## [23] Matrix.utils_0.9.7 R6_2.2.2
## [25] assertive.sets_0.0-3 locfit_1.5-9.1
## [27] AnnotationFilter_1.4.0 bitops_1.0-6
## [29] reshape_0.8.7 assertthat_0.2.0
## [31] promises_1.0.1 scales_0.5.0
## [33] nnet_7.3-12 gtable_0.2.0
## [35] affy_1.58.0 ensembldb_2.4.1
## [37] rlang_0.2.1 clisymbols_1.2.0
## [39] MatrixModels_0.4-1 genefilter_1.62.0
## [41] GlobalOptions_0.0.13 splines_3.5.0
## [43] rtracklayer_1.40.3 lazyeval_0.2.1
## [45] acepack_1.4.1 broom_0.4.4
## [47] checkmate_1.8.5 modelr_0.1.2
## [49] yaml_2.1.19 reshape2_1.4.3
## [51] GenomicFeatures_1.32.0 backports_1.1.2
## [53] httpuv_1.4.3 Hmisc_4.1-1
## [55] tools_3.5.0 psych_1.8.4
## [57] logging_0.7-103 affyio_1.50.0
## [59] assertive.strings_0.0-3 RColorBrewer_1.1-2
## [61] sessioninfo_1.0.0 Rcpp_0.12.17
## [63] plyr_1.8.4 base64enc_0.1-3
## [65] progress_1.1.2 zlibbioc_1.26.0
## [67] RCurl_1.95-4.10 prettyunits_1.0.2
## [69] dendsort_0.3.3 rpart_4.1-13
## [71] pbapply_1.3-4 GetoptLong_0.1.6
## [73] viridis_0.5.1 cowplot_0.9.2
## [75] haven_1.1.1 grr_0.9.5
## [77] ggrepel_0.8.0 cluster_2.0.7-1
## [79] magrittr_1.5 data.table_1.11.4
## [81] assertive.data_0.0-1 circlize_0.4.3
## [83] ProtGenerics_1.12.0 hms_0.4.2
## [85] mime_0.5 evaluate_0.10.1
## [87] xtable_1.8-2 XML_3.98-1.11
## [89] readxl_1.1.0 gridExtra_2.3
## [91] shape_1.4.4 compiler_3.5.0
## [93] biomaRt_2.36.1 crayon_1.3.4
## [95] R.oo_1.22.0 htmltools_0.3.6
## [97] later_0.7.2 Formula_1.2-3
## [99] geneplotter_1.58.0 lubridate_1.7.4
## [101] DBI_1.0.0 assertive.files_0.0-2
## [103] ComplexHeatmap_1.18.0 assertive.numbers_0.0-2
## [105] Matrix_1.2-14 cli_1.0.0
## [107] vsn_3.48.1 assertive.types_0.0-3
## [109] R.methodsS3_1.7.1 bindr_0.1.1
## [111] pkgconfig_2.0.1 GenomicAlignments_1.16.0
## [113] foreign_0.8-70 xml2_1.2.0
## [115] annotate_1.58.0 XVector_0.20.0
## [117] rvest_0.3.2 digest_0.6.15
## [119] ConsensusClusterPlus_1.44.0 assertive.code_0.0-1
## [121] Biostrings_2.48.0 cellranger_1.1.0
## [123] htmlTable_1.12 edgeR_3.22.2
## [125] curl_3.2 shiny_1.1.0
## [127] Rsamtools_1.32.0 rjson_0.2.19
## [129] jsonlite_1.5 nlme_3.1-137
## [131] viridisLite_0.3.0 limma_3.36.1
## [133] pillar_1.2.3 lattice_0.20-35
## [135] Nozzle.R1_1.1-1 httr_1.3.1
## [137] survival_2.41-3 interactiveDisplayBase_1.18.0
## [139] glue_1.2.0 bit_1.1-14
## [141] assertive.properties_0.0-4 stringi_1.2.2
## [143] blob_1.1.1 AnnotationHub_2.12.0
## [145] latticeExtra_0.6-28 memoise_1.1.0Jolliffe, Ian. 2002. Principal component analysis. Wiley Online Library.
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Soneson, Charlotte, Michael I Love, and Mark D Robinson. 2016. “Differential Analyses for RNA-seq: Transcript-Level Estimates Improve Gene-Level Inferences.” F1000Res. 4 (December). https://doi.org/10.12688/f1000research.7563.1.
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